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Matches in ESWC 2020 for { ?s ?p This paper presents a method for recommending new properties to Wikidata editors that can be added to enrich existing Wikidata entities. The method is designed to be effective (provide relevant properties) and efficient (in terms of computational resources). It uses a trie-based representation of the property space to efficiently calculate support for rules that are then used to recommend new properties. The paper furthermore presents various "backoff" strategies to increase the performance. The evaluation compares the method to the currently implemented Wikidata property recommender and compares different backoff variants. The paper presents a clear research problem, improving a service for a crucial and central semantic resource. The method itself is an interesting and novel combination of existing trie-methods to order properties in large knowledge graphs. The evaluation is extensive and well-described. The paper itself is well-written and easy to follow. A few points of concern: - In section 3, the recommendation task is defined to predict a relevant property, given existing properties of an item. This is a narrow definition the task, since it would make sense that not only properties, but also values could be considered. In fact, this is mentioned in the second-to-last sentence of the paper for future work. I appreciate that this is at least addressed, but I suggest to do this in section 3 already and to explain why this was not chosen in this work. - The evaluation only presents statistical comparisons between the different variants and to the existing recommender. What would clearly be an interesting extension is a human evaluation of the recommender. Are the suggested properties appreciated and or used by the editors? This is more of a suggestion for future work, which can be addressed in this paper. Especially as this paper was submitted to the Social and Human Aspects of the Semantic Web Track. All in all, the user is missing from this paper. - Related to this, even though multiple metrics are used, what is missing is some insight into the type of properties that *are* recommended in one version and not in another version. This would give more insight into the behaviour of the method. - I am also missing some discussion about the applicability of the method to other knowledge graphs. The method is now tested on one specific one, but could be applied to arbitrary (RDF) knowledge graphs as is claimed by the authors. It is now unclear what properties of Wikidata make this approach work. It might be the case that there is a specific distribution of properties that is needed for this method? A second experiment on other graphs would increase the impact of the work. Overall, I think that although the research contribution is limited, the work is interesting and well-described with a good evaluation. typos: p2 conclouding -> concluding p3 realative -> relative p14 more then -> more than ** After rebuttal: I'd like to thank the authors for their responses. I understand some of the choices that were made as explained in the rebuttal. Overall, I still think that the paper presents an interesting method and is well-written. However, I still feel that for this track, the lack of users/humans is problematic, as well as the lack of research contribution. I will keep my score.". }

• Paper.150_Review.0 hasContent "This paper presents a method for recommending new properties to Wikidata editors that can be added to enrich existing Wikidata entities. The method is designed to be effective (provide relevant properties) and efficient (in terms of computational resources). It uses a trie-based representation of the property space to efficiently calculate support for rules that are then used to recommend new properties. The paper furthermore presents various "backoff" strategies to increase the performance. The evaluation compares the method to the currently implemented Wikidata property recommender and compares different backoff variants. The paper presents a clear research problem, improving a service for a crucial and central semantic resource. The method itself is an interesting and novel combination of existing trie-methods to order properties in large knowledge graphs. The evaluation is extensive and well-described. The paper itself is well-written and easy to follow. A few points of concern: - In section 3, the recommendation task is defined to predict a relevant property, given existing properties of an item. This is a narrow definition the task, since it would make sense that not only properties, but also values could be considered. In fact, this is mentioned in the second-to-last sentence of the paper for future work. I appreciate that this is at least addressed, but I suggest to do this in section 3 already and to explain why this was not chosen in this work. - The evaluation only presents statistical comparisons between the different variants and to the existing recommender. What would clearly be an interesting extension is a human evaluation of the recommender. Are the suggested properties appreciated and or used by the editors? This is more of a suggestion for future work, which can be addressed in this paper. Especially as this paper was submitted to the Social and Human Aspects of the Semantic Web Track. All in all, the user is missing from this paper. - Related to this, even though multiple metrics are used, what is missing is some insight into the type of properties that *are* recommended in one version and not in another version. This would give more insight into the behaviour of the method. - I am also missing some discussion about the applicability of the method to other knowledge graphs. The method is now tested on one specific one, but could be applied to arbitrary (RDF) knowledge graphs as is claimed by the authors. It is now unclear what properties of Wikidata make this approach work. It might be the case that there is a specific distribution of properties that is needed for this method? A second experiment on other graphs would increase the impact of the work. Overall, I think that although the research contribution is limited, the work is interesting and well-described with a good evaluation. typos: p2 conclouding -> concluding p3 realative -> relative p14 more then -> more than ** After rebuttal: I'd like to thank the authors for their responses. I understand some of the choices that were made as explained in the rebuttal. Overall, I still think that the paper presents an interesting method and is well-written. However, I still feel that for this track, the lack of users/humans is problematic, as well as the lack of research contribution. I will keep my score."".